Martin Steinegger

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Milot MirditaSeoul National UniversityEmail yang diverifikasi di snu.ac.kr
Johannes SödingMax-Planck-Institute for Multidisciplinary Sciences, GöttingenEmail yang diverifikasi di mpinat.mpg.de
Burkhard RostProfessor of Computer Science, Technical University MunichEmail yang diverifikasi di tum.de
Michael HeinzingerTechnische Universität MünchenEmail yang diverifikasi di tum.de
Sergey OvchinnikovMITEmail yang diverifikasi di mit.edu
Christian DallagoNVIDIA & Technische Universität MünchenEmail yang diverifikasi di tum.de
Ahmed ElnaggarTechnical University of MunichEmail yang diverifikasi di tum.de
Steven SalzbergBloomberg Distinguished Professor, Johns Hopkins UniversityEmail yang diverifikasi di jhu.edu
Debsindhu BhowmikComputational Scientist, Oak Ridge National LaboratoryEmail yang diverifikasi di ornl.gov
Antonio Fernàndez-GuerraCentre for Ancient Environmental GenomicsEmail yang diverifikasi di sund.ku.dk
Clovis GaliezGrenoble-Alpes University, Grenoble INP, LJKEmail yang diverifikasi di grenoble-inp.fr
Tom O. DelmontCNRS - Genoscope - TARA anvi-OceansEmail yang diverifikasi di genoscope.cns.fr
John JumperDeepMindEmail yang diverifikasi di google.com
Prof. Dr. Frank Oliver GlöcknerAlfred Wegener Institute Bremerhaven, MARUM, University of BremenEmail yang diverifikasi di awi.de
Maria J. MartinEuropean Bioinformatics InstituteEmail yang diverifikasi di ebi.ac.uk
Vikram AlvaGroup Leader, Max Planck Institute for BiologyEmail yang diverifikasi di tuebingen.mpg.de
Andrei LupasDirector of the Department of Protein Evolution, Max-Planck-Institute for BiologyEmail yang diverifikasi di tuebingen.mpg.de
Christof AngermuellerGoogleEmail yang diverifikasi di google.com
koray kavukcuogluDeepMindEmail yang diverifikasi di kavukcuoglu.org
Demis HassabisDeepMind

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Martin Steinegger

Associate Professor, Seoul National University, Korea

Email yang diverifikasi di snu.ac.kr - Beranda

Bioinformatics Computational Biology


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Nature, 2021	33001	2021
ColabFold: making protein folding accessible to all M Mirdita, K Schütze, Y Moriwaki, L Heo, S Ovchinnikov, M Steinegger Nature Methods, 679–682, 2022	6621	2022
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets M Steinegger, J Söding Nature biotechnology 35 (11), 1026-1028, 2017	3015	2017
ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing A Elnaggar, M Heinzinger, C Dallago, G Rehawi, Y Wang, L Jones, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 14 (8), 2021	1875*	2021
Fast and accurate protein structure search with Foldseek M van Kempen, SS Kim, C Tumescheit, M Mirdita, J Lee, CLM Gilchrist, ... Nature Biotechnology 42, 243–246, 2023	1270*	2023
HH-suite3 for fast remote homology detection and deep protein annotation M Steinegger, M Meier, M Mirdita, H Vöhringer, SJ Haunsberger, J Söding BMC Bioinformatics 20, 2019	1030	2019
Clustering huge protein sequence sets in linear time M Steinegger, J Söding Nature communications 9 (1), 2542, 2018	800	2018
Uniclust databases of clustered and deeply annotated protein sequences and alignments M Mirdita, L von den Driesch, C Galiez, MJ Martin, J Söding, M Steinegger Nucleic Acids Research, 2016	693	2016
Protein sequence analysis using the MPI bioinformatics toolkit F Gabler, SZ Nam, S Till, M Mirdita, M Steinegger, J Söding, AN Lupas, ... Current Protocols in Bioinformatics 72 (1), e108, 2020	669	2020
AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences M Varadi, D Bertoni, P Magana, U Paramval, I Pidruchna, ... Nucleic Acids Research, 2023	576	2023
MMseqs2 desktop and local web server app for fast, interactive sequence searches M Mirdita, M Steinegger, J Söding Bioinformatics 35 (16), 2856–2858, 2019	476	2019
Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold M Steinegger, M Mirdita, J Söding Nature Methods 16, 603–606, 2019	424	2019
Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021	369	2021
Metagenome analysis using the Kraken software suite J Lu, N Rincon, DE Wood, FP Breitwieser, C Pockrandt, B Langmead, ... Nature Protocols, 2022	344	2022
PredictProtein - Predicting Protein Structure and Function for 29 Years M Bernhofer, C Dallago, T Karl, V Satagopam, M Heinzinger, M Littmann, ... Nucleic Acids Research, 2021	236	2021
MMseqs software suite for fast and deep clustering and searching of large protein sequence sets M Hauser, M Steinegger, J Söding Bioinformatics 32 (9), 1323-1330, 2016	228	2016
High accuracy protein structure prediction using deep learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, ... Fourteenth critical assessment of techniques for protein structure …, 2020	208*	2020
Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank M Steinegger, SL Salzberg Genome Biology 21, 2020	200	2020
Clustering predicted structures at the scale of the known protein universe I Barrio-Hernandez, J Yeo, J Jänes, M Mirdita, CLM Gilchrist, T Wein, ... Nature, 2023	195	2023
Fast and sensitive taxonomic assignment to metagenomic contigs M Mirdita, M Steinegger, F Breitwieser, J Söding, E Levy Karin Bioinformatics, 2021	193	2021

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